Numerical Study on Reduction in Aerodynamic Drag and Noise of High-Speed Pantograph

Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly,energy efficient and rapid advances in train technology.Using computational fluid dynamics theory and the K-FWH acoustic equation,a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs.A component optimization method is proposed as a possible solution to the problemof aerodynamic drag and noise in high-speed pantographs.The results of the study indicate that the panhead,base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs.Therefore,a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise.By optimizing the cross-sectional shape of the strips and insulators,the drag and noise caused by airflow separation and vortex shedding can be reduced.The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest.Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph.In addition,the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise.In contrast to the original pantograph design,the improved pantograph shows a 21.1%reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

Physical Layer Encryption of OFDM-PON Based on Quantum Noise Stream Cipher with Polar Code

Orthogonal frequency division multiplexing passive optical network(OFDM-PON) has superior anti-dispersion property to operate in the C-band of fiber for increased optical power budget. However,the downlink broadcast exposes the physical layer vulnerable to the threat of illegal eavesdropping. Quantum noise stream cipher(QNSC) is a classic physical layer encryption method and well compatible with the OFDM-PON. Meanwhile, it is indispensable to exploit forward error correction(FEC) to control errors in data transmission. However, when QNSC and FEC are jointly coded, the redundant information becomes heavier and thus the code rate of the transmitted signal will be largely reduced. In this work, we propose a physical layer encryption scheme based on polar-code-assisted QNSC. In order to improve the code rate and security of the transmitted signal, we exploit chaotic sequences to yield the redundant bits and utilize the redundant information of the polar code to generate the higher-order encrypted signal in the QNSC scheme with the operation of the interleaver.We experimentally demonstrate the encrypted 16/64-QAM, 16/256-QAM, 16/1024-QAM, 16/4096-QAM QNSC signals transmitted over 30-km standard single mode fiber. For the transmitted 16/4096-QAM QNSC signal, compared with the conventional QNSC method, the proposed method increases the code rate from 0.1 to 0.32 with enhanced security.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

Predictors of Adverse Pregnancy Outcomes Following Traumatic Injuries

Objective After traumatic injury in pregnant women,providing timely and appropriate management for high-risk patients is crucial for both pregnant women and fetuses.This study aimed to identify risk factors that predict adverse pregnancy outcomes after traumatic injury.Methods A retrospective cohort study including 317 pregnant patients who experienced trauma was conducted.The collected data included general demographics,injury mechanisms and adverse pregnancy outcomes.Patients were divided into two subgroups based on the absence or presence of trauma-related adverse pregnancy outcomes.Univariate and multivariate logistic regressions were conducted to estimate the associations between clinical variables and adverse pregnancy outcomes.Results A total of 41(12.93%)patients experienced adverse pregnancy outcomes within the first 24 h post-trauma.This study revealed that age>35 years(OR=14.995,95%CI:5.024–44.755,P<0.001),third trimester trauma(OR=3.878,95%CI:1.343–11.204,P=0.012),abdominal pain(OR=3.032,95%CI:1.221–7.527,P=0.017),vaginal bleeding(OR=3.226,95%CI:1.093–9.523,P=0.034),positive scan in focused assessment with sonography for trauma(FAST)positive(OR=8.496,95%CI:2.825–25.555,P<0.001),9≤injury severity score(ISS)<16(OR=3.039,95%CI:1.046–8.835,P=0.041)and ISS≥16(OR=5.553,95%CI:1.387–22.225,P=0.015)increased the probability of posttraumatic adverse pregnancy outcomes.Maternal age,gestational age at delivery,vaginal bleeding and positive FAST results were risk factors for abnormal delivery.Conclusion Advanced maternal age,third trimester,and positive FAST results should alert multidisciplinary trauma teams to closely monitor patients to prevent adverse pregnancy outcomes.

Predictive Effect of CA125 on Adverse Cardiovascular Events in Patients with Chronic Heart Failure

Objective: To study the expression of CA125 in the serum of patients with CHF and the relationship between CA125 level and the occurrence of adverse cardiovascular events. Methods: The clinical data of 132 patients with CHF admitted to Shizuishan Second People’s Hospital from January 2023 to December 2023 were collected and divided into heart function II group, heart function III group, heart function IV group according to cardiac function. 44 healthy subjects who underwent physical examination during the same period were selected as the control group. The clinical data of CA125, NT-proBNP, echocardiography and other clinical data of the four groups were compared, and the incidence of major adverse cardiovascular events was followed up for 12 months. Results: Compared with the control group, the CA125 level in the CHF group was significantly increased (P Conclusion: Serum CA125 level is related to the cardiac function level in CHF patients and increases with the deterioration of cardiac function. The increase of the index is related to the mortality rate and re-hospitalization rate, suggesting that CA125 can be used as an indicator to reflect the severity of heart failure and prognosis monitoring.

Vibration and noise mechanism of a 110 kV transformer under DC bias based on finite element method

Global energy and environmental issues are becoming increasingly problematic,and the vibration and noise problem of 110 kV transformers,which are the most widely distributed,have attracted widespread attention from both inside and outside the industry.DC bias is one of the main contributing factors to vibration noise during the normal operation of transformers.To clarify the vibration and noise mechanism of a 110 kV transformer under a DC bias,a multi-field coupling model of a 110 kV transformer was established using the finite element method.The electromagnetic,vibration,and noise characteristics during the DC bias process were compared and quantified through field circuit coupling in parallel with the power frequency of AC,harmonic,and DC power sources.It was found that a DC bias can cause significant distortions in the magnetic flux density,force,and displacement distributions of the core and winding.The contributions of the DC bias effect to the core and winding are different at Kdc=0.85.At this point,the core approached saturation,and the increase in the core force and displacement slowed.However,the saturation of the core increased the leakage flux,and the stress and displacement of the winding increased faster.The sound field distribution characteristics of the 110 kV transformer under a DC bias are related to the force characteristics.When the DC bias coefficient was 1.25,the noise sound pressure level reached 73.6 dB.

Adverse drug reactions of first-line antitubercular drugs:A retrospective study on characteristics,management,factors,and impacts

Objective:To elucidate the characteristics,management strategies,risk factors,and clinical impacts associated with adverse drug reactions(ADRs)induced by first-line antitubercular drugs to enhance tuberculosis(TB)management.Methods:A retrospective cohort study was conducted by retrieving drug-susceptible TB records among adult patients who received TB treatment from 2018 to 2021 at 10 public health clinics in Sarawak,Malaysia.Only the initial TB treatment and occurrence of specific ADRs within the study period were considered.Regression analysis was performed to identify the risk factors associated with both overall ADRs and individual types of ADRs.Results:Among 2953 cases,705(23.9%)developed ADRs.Cutaneous reactions were the most prevalent(47.1%),followed by hepatotoxicity(32.8%)and gastrointestinal disturbances(29.8%).Six out of seven types of ADRs investigated occurred within the intensive phase,mostly manifesting at approximately 2 weeks of initiation.Hepatotoxicity resulted in the majority(85.3%)of treatment discontinuations,while vision problems led to treatment modifications in half of the cases.Risk factors for all ADRs included age≥60 years,females,illicit drug use,and comorbidities such as HIV-positive,diabetes,and chronic liver disease.Alcohol consumption was independently associated with hepatotoxicity.ADRs caused around one-third of interruptions exceeding 2 weeks(33.0%)and subsequently necessitated treatment restarts(34.5%).Conclusions:Understanding these various aspects contributes to improving the overall management of ADRs in TB treatment.Close ADR monitoring and reporting are essential to strengthen ADR management.

Why Don’t We Adequately Identify and Manage Adverse Drug Reactions despite Having the Needed Information?

Importance/Objective: Adverse Drug Reactions (ADRs) are unavoidable, but recognizing and addressing ADRs early can improve wellness and prevent permanent injury. We suggest that available medical information and digital/electronic methods could be used to manage this major healthcare problem for individual patients in real time. Methods: We searched the available digital applications and three literature databases using the medical subject heading terms, adverse drug reaction reporting systems or management, filtered by clinical trial or systemic reviews, to detect publications with data about ADR identification and management approaches. We reviewed the reports that had abstract or summary data or proposed or implemented methods or systems with potential to identify or manage ADRs in clinical settings. Results: The vast majority of the 481 reports used retrospectively collected data for groups of patients or were limited to surveying one population group or class of medication. The reports showed potential and definite associations of ADRs for specific drugs and problems, mostly, but not exclusively, for patients in hospitals and nursing homes. No reports described complete methods to collect comprehensive data on ADRs for individual patients in a healthcare system. The digital applications have ADR information, but all are too cumbersome or incomplete for use in active clinical settings. Several studies suggested that providing information about potential ADRs to clinicians can reduce these problems. Conclusion and Relevance: Although investigators and government agencies agree with the need, there is no comprehensive ADR management program in current use. Informing the patient’s healthcare practitioners of potential ADRs at the point of service has the potential for reduction of these complications, which should improve healthcare and reduce unneeded costs.

Robotic-assisted versus laparoscopic repair of type II, III and IV hiatal hernias: A retrospective study comparing adverse outcomes

Objective:Robotic-assisted surgery(RAS)is continuing to expand in use in surgical specialties,including foregut surgery.The available data on its use in large hiatal hernia(HH)repair are limited and conflicting.This study sought to determine whether there are significant differences in adverse outcomes following HH repair performed with a robotic approach vs.a laparoscopic approach.This study was limited to outcomes in patients with type II,III,and IV HHs,as these hernias are typically more challenging to repair.Methods:A retrospective analysis was performed from data obtained from TriNetX,a large deidentified clinical database,over a 10-year period.Adult patients who underwent type II,III,or IV HH repair were included in the study.HH with robotic repair was compared to laparoscopic repair.Cohorts were propensity score matched for demographic information and comorbidities.Risk ratios,risk differences(RDs)with 95%confidence intervals(CIs),and t test for each examined adverse outcome were used to estimate the effects of robotic repair vs.laparoscopic repair.Results:In total,20,016 patients who met the inclusion criteria were identified;1,515 patients utilized RAS,and 18,501 used laparoscopy.Prior to matching,there were significant differences in age,sex,comorbidity,and BMI between the two cohorts.After 1:1 propensity score matching,analyses of 1,514 well-matched patient pairs revealed no significant differences in demographics or comorbidities.Patients who underwent robotic repair were more likely to experience major complications,including venous thromboembolism(RD:0.007,95%CI:0.003,0.011;p?0.002),critical care(RD:0.023,95%CI:0.007,0.039;p?0.004),urinary/renal complications(RD:0.027,95%CI:0.014,0.041;p<0.001),and respiratory complications(RD:0.046,95%CI:0.028,0.064;p<0.001).RAS was associated with a significantly shorter length of stay(32.4±27.5 h vs.35.7±50.1 h,p?0.031),although this finding indicated a reduction in the length of stay of less than 4 hours.No statistically significant differences in risk of esophageal perforation,infection,postprocedural shock,bleeding,mortality,additional emergency room visits,cardiac complications,or wound disruption were found.Conclusions:Patients who undergo robotic-assisted large HH repair are at increased risk of venous thromboembolism,need critical care,urinary or renal complications and respiratory complications.Due to variations in RAS technique,experience,and surgical volumes,further study of this surgical approach and complication rates is warranted.

Adaptive Bistable Stochastic Resonance Based Weak Signal Reception in Additive Laplacian Noise

Weak signal reception is a very important and challenging problem for communication systems especially in the presence of non-Gaussian noise,and in which case the performance of optimal linear correlated receiver degrades dramatically.Aiming at this,a novel uncorrelated reception scheme based on adaptive bistable stochastic resonance(ABSR)for a weak signal in additive Laplacian noise is investigated.By analyzing the key issue that the quantitative cooperative resonance matching relationship between the characteristics of the noisy signal and the nonlinear bistable system,an analytical expression of the bistable system parameters is derived.On this basis,by means of bistable system parameters self-adaptive adjustment,the counterintuitive stochastic resonance(SR)phenomenon can be easily generated at which the random noise is changed into a benefit to assist signal transmission.Finally,it is demonstrated that approximately 8dB bit error ratio(BER)performance improvement for the ABSR-based uncorrelated receiver when compared with the traditional uncorrelated receiver at low signal to noise ratio(SNR)conditions varying from-30dB to-5dB.

Adverse Events following AstraZeneca COVID-19 Vaccination: A Case Study in Abidjan, Côte d’Ivoire

Introduction: Pharmaceutical companies have boosted vaccine production following the global COVID-19 pandemic. In Côte d’Ivoire, the first vaccination campaign with the AstraZeneca vaccine began on March 1, 2021, as part of the Covax program. Despite the positive benefit/risk balance, the adverse effects of vaccination should not be minimized. Objective: To identify adverse events of AstraZeneca’s COVID-19 vaccination for better management. Materials and Methods: This is a case of a 57-year-old obese (BMI = 39 kg/m2) female health care worker who experienced adverse events in March 2021 after the second dose of AstraZeneca vaccine administered 4 weeks apart. These were subject to mandatory case reporting. Results: Major post-vaccination events occurred in a noisy systemic picture with parameters showing significant disturbances. Biological surveillance remains costly and makes the accountability of the vaccine complex. Conclusion: Vaccination remains the ultimate weapon in the fight against endemic diseases but should not overshadow the reporting of adverse events.

Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces.Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs.However,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise.It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization.Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation structure.Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird sounds.Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct.By clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio:Markers predicting immune-checkpoint inhibitor efficacy and immune-related adverse events

We conducted a comprehensive review of existing prediction models pertaining to the efficacy of immune-checkpoint inhibitor(ICI)and the occurrence of immune-related adverse events(irAEs).The predictive potential of neutrophil-to-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR)in determining ICI effectiveness has been extensively investigated,while limited research has been conducted on predicting irAEs.Furthermore,the combined model incor-porating NLR and PLR,either with each other or in conjunction with additional markers such as carcinoembryonic antigen,exhibits superior predictive capabilities compared to individual markers alone.NLR and PLR are promising markers for clinical applications.Forthcoming models ought to incorporate established efficacious models and newly identified ones,thereby constituting a multifactor composite model.Furthermore,efforts should be made to explore effective clinical application approaches that enhance the predictive accuracy and efficiency.

Performance of the EDACS-ADP incorporating high-sensitivity troponin assay:Do components of major adverse cardiac events matter?

BACKGROUND:The accelerated diagnostic protocol(ADP)using the Emergency Department Assessment of Chest pain Score(EDACS-ADP),a tool to identify patients at low risk of a major adverse cardiac event(MACE)among patients presenting with chest pain to the emergency department,was developed using a contemporary troponin assay.This study was performed to validate and compare the performance of the EDACS-ADP incorporating high-sensitivity cardiac troponin I between patients who had a 30-day MACE with and without unstable angina(MACE I and II,respectively).METHODS:A single-center prospective observational study of adult patients presenting with chest pain suggestive of acute coronary syndrome was performed.The performance of EDACS-ADP in predicting MACE was assessed by calculating the sensitivity and negative predictive value.RESULTS:Of the 1,304 patients prospectively enrolled,399(30.6%;95%confidence interval[95%CI]:27.7%–33.8%)were considered low-risk using the EDACS-ADP.Among them,the rates of MACE I and II were 1.3%(5/399)and 1.0%(4/399),respectively.The EDACS-ADP showed sensitivities and negative predictive values of 98.8%(95%CI:97.2%–99.6%)and 98.7%(95%CI:97.0%–99.5%)for MACE I and 98.7%(95%CI:96.8%–99.7%)and 99.0%(95%CI:97.4%–99.6%)for MACE II,respectively.CONCLUSION:EDACS-ADP could help identify patients as safe for early discharge.However,when unstable angina was added to the outcome,the 30-day MACE rate among the designated lowrisk patients remained above the level acceptable for early discharge without further evaluation.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

Research on the generation method of seawater sound velocity model based on Perlin noise

In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity model.However,due to static pressure,temperature difference and other factors,random disturbances may often frequently in seawater bodies.The impact of such disturbances on data processing results is a topic of theoretical research.Since seawater sound velocity is a difficult physical quantity to measure,there is a need for a method that can generate models conforming to seawater characteristics.This article will combine the Munk model and Perlin noise to propose a two-dimensional dynamic seawater sound velocity model generation method,a method that can generate a dynamic,continuous,random seawater sound velocity model with some regularity at large scales.Moreover,the paper discusses the influence of the inhomogeneity characteristics of seawater on wave field propagation and imaging.The results show that the seawater sound velocity model with random disturbance will have a significant influence on the wave field simulation and imaging results.

Impact of body mass index on adverse kidney events in diabetes mellitus patients: A systematic-review and meta-analysis

BACKGROUND The incidence of chronic kidney disease among patients with diabetes mellitus(DM)remains a global concern.Long-term obesity is known to possibly influence the development of type 2 diabetes mellitus.However,no previous meta-analysis has assessed the effects of body mass index(BMI)on adverse kidney events in patients with DM.AIM To determine the impact of BMI on adverse kidney events in patients with DM.METHODS A systematic literature search was performed on the PubMed,ISI Web of Science,Scopus,Ovid,Google Scholar,EMBASE,and BMJ databases.We included trials with the following characteristics:(1)Type of study:Prospective,retrospective,randomized,and non-randomized in design;(2)participants:Restricted to patients with DM aged≥18 years;(3)intervention:No intervention;and(4)kidney adverse events:Onset of diabetic kidney disease[estimated glomerular filtration rate(eGFR)of<60 mL/min/1.73 m2 and/or microalbuminuria value of≥30 mg/g Cr],serum creatinine increase of more than double the baseline or end-stage renal disease(eGFR<15 mL/min/1.73 m2 or dialysis),or death.RESULTS Overall,11 studies involving 801 patients with DM were included.High BMI(≥25 kg/m2)was significantly associated with higher blood pressure(BP)[systolic BP by 0.20,95%confidence interval(CI):0.15–0.25,P<0.00001;diastolic BP by 0.21 mmHg,95%CI:0.04–0.37,P=0.010],serum albumin,triglycerides[standard mean difference(SMD)=0.35,95%CI:0.29–0.41,P<0.00001],low-density lipoprotein(SMD=0.12,95%CI:0.04–0.20,P=0.030),and lower high-density lipoprotein(SMD=–0.36,95%CI:–0.51 to–0.21,P<0.00001)in patients with DM compared with those with low BMIs(<25 kg/m2).Our analysis showed that high BMI was associated with a higher risk ratio of adverse kidney events than low BMI(RR:1.22,95%CI:1.01–1.43,P=0.036).CONCLUSION The present analysis suggested that high BMI was a risk factor for adverse kidney events in patients with DM.

Effect of Bogie Cavity End Wall Inclination on Flow Field and Aerodynamic Noise in the Bogie Region of High-Speed Trains

Combining the detached eddy simulation(DES)method and Ffowcs Williams-Hawkings(FW-H)equation,the effect of bogie cavity end wall inclination on the flow field and aerodynamic noise in the bogie region is numerically studied.First,the simulation is conducted based on a simplified cavity-bogie model,including five cases with different inclination angles of the front and rear walls of the cavity.By comparing and analyzing the flow field and acoustic results of the five cases,the influence of the regularity and mechanism of the bogie cavity end wall inclination on the flow field and the aerodynamic noise of the bogie region are revealed.Then,the noise reduction strategy determined by the results of the simplified cavity-bogie model is applied to a three-car marshaling train model to verify its effectiveness when applied to the real train.The results reveal that the forward inclination of the cavity front wall enlarges the influence area of shear vortex structures formed at the leading edge of the cavity and intensifies the interaction between the vortex structures and the front wheelset,frontmotor,and front gearbox,resulting in the increase of the aerodynamic noise generated by the bogie itself.The backward inclination of the cavity rear wall is conducive to guiding the vortex structures flow out of the cavity and weakening the interaction between the shear vortex structures and the cavity rear wall,leading to the reduction of the aerodynamic noise generated by the bogie cavity.Inclining the rear end wall of the foremost bogie cavity of the head car is a feasible aerodynamic noise reduction measure for high-speed trains.